Individual pieces of music are identified herein as “songs” for simplicity, regardless of whether such songs actually involve any form of human singing. Rather, a song is an individual piece of music that has a beginning and an end, regardless of its length, the type of music being played therein, whether it is instrumental, vocal or a combination of both, and regardless of whether it is part of a collection of songs, such as an album, or by itself, a single.
Traditional content selection systems, especially music selections systems, such as APPLE ITUNES, tend to rely on content types based on style, genre, content author(s), content performer(s), etc., for enabling users to browse through vast libraries of content and make selections to watch, listen, rent, buy, etc. For example, in such music selection systems, the music is often organized by the genre, style or type of music, i.e., jazz, classical, hip hop, rock and roll, electronic, etc., and within such genres, the music may be further classified by the artist, author, record label, era (i.e., 50's rock), etc.
Some music selection systems will also make recommendations for music based on user preferences and other factors. Pandora Media, Inc.'s PANDORA radio system, for example, allows users to pick music based on genre and artists, and will then recommend additional songs the user may be interested in listening to based on the user's own identification system. This identification system is derived from the Music Genome Project. While the details of the Music Genome Project do not appear to be publicly available, certain unverified information about it is available on-line. For example, Wikipedia states that the Music Genome Project uses over 450 different musical attributes, combined into larger groups called focus traits, to make these recommendations. There are alleged to be thousands of focus traits, including rhythm syncopation, key tonality, vocal harmonies, and displayed instrumental proficiency. See, http://en.wikipedia.org/wiki/Music_Genome_Project.
According to Wikipedia, in accordance with the Music Genome Project, each song is represented by a vector (a list of attributes) containing up to 450 or more attributes or “genes,” as noted above. Each gene corresponds to a characteristic of the music, for example, gender of lead vocalist, level of distortion on the electric guitar, type of background vocals, etc. Different genres of music will typically have different sets of genes, e.g., 150 genes for some types of music, 350 to 400 genes for other types, and as many as 450 genes for some forms of classical music. Each gene is assigned a number between 0 and 5, in half-integer increments. The assignment is performed by a human in a process that takes 20 to 30 minutes per song. Some percentage of the songs is further analyzed by other humans to ensure conformity. Distance functions are used to develop lists of songs related to a selected song based on the vector assigned to the selected song.
While the Music Genome Project represents an ambitious and detailed identification system, it suffers from many shortcomings as a result of its inherent complexity. The most significant of these deficiencies is that it often recommends songs, as implemented by PANDORA, as being similar to other songs, but listeners of those songs are not capable of identifying why those songs were determined to be similar. There may be very good reasons, among the hundreds of attributes being used to make determinations of similarities between the songs, but those similarities do not appear to relate to what most listeners hear or feel. Accordingly, a better, more simplistic solution is needed.
Human identification relies on human perception, which is a subjective system. Human perception is believed to be involved because songs identified by a particular mood and a particular color may or may not sound anything like other songs identified by the same mood and color. This tends to indicate human perception as a subjective error factor in identifying music in this manner.